GIS and Health
Geography
What is epidemiology?

GIS and health geography
◦ Major applications for GIS

Epidemiology
◦ What is health (and how location matters)
◦ What is a disease (and how to identify one)
◦ Quantifying disease occurrence
 Incidence vs prevalence
 Identifying the population
 Working with small area data
TOC

A GIS can be a useful tool for health
researchers and planners because, as
expressed by Scholten and Lepper (1991):
◦ Health and ill-health are affected by a variety of lifestyle and environmental factors, including where
people live. Characteristics of these locations
(including socio-demographic and environmental
exposure) offer a valuable source for epidemiological
research studies on health and the environment.
Health and ill-health always have a spatial dimension
therefore. More than a century ago, epidemiologists
and other medical scientists began to explore the
potential of maps for understanding the spatial
dynamics of disease.
GIS and health Geography
1.
Spatial epidemiology
2.
Environmental hazards
3.
Modeling Health Services
4.
Identifying health inequalities
Major applications for GIS

Spatial epidemiology is concerned with
describing and understanding spatial
variation in disease risk.
 Individual level data
 Counts for small areas

Recent developments owe much to:
 Geo-referenced health and population data
 Computing advances
 Development of GIS
 Statistical methodology
Spatial epidemiology
Population is unevenly distributed
geographically.
 People move around (day-to-day
movements; longer term movements
including migration).
 People possess relevant individual
characteristics (age, sex, genetic makeup, lifestyle, etc).
 People live in communities (small areas).

Framework for analysis

Provides a qualitative answer
about the existence of an
association (e.g. between
environmental variable and
health outcome).

May provide evidence that can
be followed up in other ways.
Why small area analyses?
These studies typically involve
examining geographical
variations in exposure to
environmental variables (air,
water, soil, etc.) and their
association with health
outcomes while controlling for
other relevant factors using
regression.
Geographical correlation studies
Issues: Spatial misalignment
Frequency and quality of population data (e.g.
Census every 10 years).
 Spatial compatibility of different data sets.
 Availability of data on population movements.
 Measuring population exposure to the
environmental variable.
 Environmental impacts are often likely to be
quite small (relative to, for example, lifestyle
effects) and there may be serious confounding
effects.
 Cannot estimate strength of an association;
 Ecological (or aggregation) bias.

Issues: Uncertainty
 Allow
for heterogeneity of
exposure.
 Use well defined population groups.
 Use survey data to help obtain
good exposure data.
 Allow for latency times.
 Allow for population movement
effects.
Issues: Best practices
(Richardson 1992)

Dr. John
Snow’s Map
of Cholera
Deaths in the
SOHO District
of London,
1854
Spatial epidemiology
1.
Spatial epidemiology
2.
Environmental hazards
3.
Modeling Health Services
4.
Identifying health inequalities
Major applications for GIS
Hazard
Surveillance
•Hazardous agent present
in the environment
•Route of exposure exists
•Host exposed to agent
•Agent reaches target
tissue
Exposure •Agent produces adverse
Surveillance effect
Outcome
Surveillance
•Effect clinically apparent
Environmental hazards
GIS: Identify causal and mitigating factors
Environmental hazards
1.
Spatial epidemiology
2.
Environmental hazards
3.
Modeling Health Services
4.
Identifying health inequalities
Major applications for GIS

A generic index of accessibility/
remoteness for all populated places in
non-metropolitan Australia

A model which allows accessibility to
any type of service to be calculated
from all populated places in Australia
ARIA (Accessibility/Remoteness
Index of Australia)
AIRA
Geographical location
“Where do infants and children die in WA? 1980-2002”
Jane Freemantle, PhD. November 2004
Remote
non-Aboriginal
Rural
Aboriginal
Metro.
0
2
4
6
8 10 12 14 16 18 20 22 24 26
Mortality Rate / 1000 live births
Mortality rate of infants (1980-2001)
Identifying health inequalities:
Well-known relationship
◦ 25% – 50% of observed gradient due to risk
factors like smoking, hypertension and diabetes in
lower socio-economic groups (Marmot et al.,1997)
◦ Access to healthcare (Bosma et al., 2005)
◦ Imbalance between workplace demands and
economic reward (Lynch et al.,1997)
◦ Poor education, lower levels of health literacy, low
birth weight (Marmot, 2000)
Relationship may vary with gender with the
association thought to be stronger in males
(Thurston, 2005)
SES and Heart disease

Number of daily hospital discharges (Y) with
Ischemic Heart Disease (IHD) where
admission had been via emergency room for
◦
◦
◦
◦
591 postcodes in NSW
Every day from July 1, 1996 to June 30, 2001
Males and females
5-year age increments
Denominator (N) obtained from census
 Social disadvantage measured at postal area
level using the census-derived SEIFA (SocioEconomic Indexes for Areas) index

The Data
High values indicate
social advantage
SEIFA distribution in NSW
NSW IHD rates

GIS and health geography
◦ Major applications for GIS

Epidemiology
◦ What is health (and how location matters)
◦ What is a disease (and how to identify one)
◦ Quantifying disease occurrence
 Incidence vs prevalence
 Identifying the population
 Working with small area data
TOC

The study of the distribution and determinants
of health and disease-related states in
populations, and the application of this study
to control health problems.

‘the product of [epidemiology] is research and
information and not public health action and
implementation’
(Atwood et al. 1997)

‘epidemiology’s full value is achieved only
when its contributions are placed in the
context of public health action, resulting in a
healthier populace.’ (Koplan et al. 1999)
What is epidemiology?

… are like bookies of disease, stalking the
globe to determine point-spreads on which
groups of people are most likely to get which
diseases.

Part detective and part statistician, part
anthropologist and part physician,
epidemiologists hope to track down the agents
of illness by deducing which of the differences
between peoples lie at the root of their
distinctive disease patterns.
Epidemiologists . . .
(H. Shodell, Science ’82, September, p. 50)
DESCRIPTIVE
Health and disease in the community
What?
Who?
When?
Where?
What are the
health problems
of the
community?
How many people
are affected?
Over what
period of time?
Where do the
affected people
live, work or
spend leisure
time?
What are the
attributes of
these illnesses?
ANALYTIC
Why?
What are the
causal agents?
What factors
affect outcome?
What are the
attributes of
affected persons?
Etiology, prognosis and program evaluation
How?
By what mechanism
do they operate?
Epidemiologic approaches
Dorland's Illustrated Medical Dictionary (28th
ed.):
Health – "a state of optimal physical, mental, and
social well-being, and not merely the absence of
disease and infirmity.“
Disease – "any deviation from or interruption of the
normal structure or function of any part, organ, or
system (or combination thereof) of the body that is
manifested by a characteristic set of symptoms and
signs . . .".
What are “disease” and “health”?

Health, as defined in the World Health
Organization's Constitution, is "a state of complete
physical, mental and social well-being and not
merely the absence of disease or infirmity."

Health is seen as more than just the absence of
disease, and depends upon a complex suite of
factors, with location taking the lead. A location is
more than just a position within a spatial frame
(e.g., on the surface of the Earth or within the
human body).

Different locations on Earth are usually associated
with different profiles: physical, biological,
environmental, economic, social, cultural and
possibly even spiritual profiles, that do affect and
are affected by health, disease and healthcare.
What is ‘health’

An example of how location matters and carries with it other
factors into play

The body weight of infants at birth is one readily available piece
of data, and the relationship between low birth-weight and
maternal and child health is a continuing line of research.

In New York City, Sara McLafferty and Barbara Tempalski have
studied the spatial distribution of low birth-weight infants and
identified areas in which the number of low birth-weight infants
increased sharply during the 1980s.

Their results indicated that the rise in low birth-weight was
closely linked to women's declining economic status, inadequate
insurance coverage and prenatal care, as well as the spread of
crack/cocaine.
Location and health
Location and health
Location and health

GIS and health geography
◦ Major applications for GIS

Epidemiology
◦ What is health (and how location matters)
◦ What is a disease (and how to identify
one)
◦ Quantifying disease occurrence
 Incidence vs prevalence
 Identifying the population
 Working with small area data
TOC
Manifestional criteria:
Manifestational criteria refer to symptoms, signs, and
other manifestations of the condition. Defining a disease
in terms of manifestational criteria relies on the
proposition that diseases have a characteristic set of
manifestations. This defines disease in terms of labeling
symptoms.
Causal criteria:
Causal criteria refer to the etiology of the condition,
which must have been identified in order to be employed.
This defines disease in terms of underlying pathological
etiology.
What is ‘disease’

How do you identify a disease?

The Acquired Immunodeficiency Syndrome (AIDS) was
initially defined by the CDC in terms of manifestational
criteria as a basis for instituting surveillance.

The operational definition grouped diverse manifestations
– Kaposi's sarcoma outside its usual subpopulation, PCP
and other opportunistic infections in people with no
known basis for immunodeficiency.

This was based on similar epidemiologic observations
(similar population affected, similar geographical
distribution) and a shared type immunity deficit (elevated
ratio of T-suppressor to T-helper lymphocytes).
Manifestational Criteria

Human immunodeficiency virus (HIV,
previously called human lymphotrophic
virus type III) was discovered and
demonstrated to be the causal agent
for AIDS.

AIDS could then be defined by causal
criteria.
Causal Criteria

A single causal agent may have multiple
clinical effects.

Multiple etiologic pathways may lead to
apparently identical manifestations, so that a
manifestationally-defined disease entity may
include subgroups with differing etiologies.

Multi-causation necessitates a degree of
arbitrariness in assigning a causative versus a
contributing factor to a disease.

Not all persons with the causal agent develop
the disease.
Challenges with Disease Classifications
Onset of
disease
Physiologic
Underlying Abnormalities
Genetic
Susceptibility
Diagnosis
of disease
Sub-clinical disease
Cause-specific
mortality
Clinical disease
Environmental & Behavioral Factors
(Spatial dependence)
The natural history of disease
X

GIS and health geography
◦ Major applications for GIS

Epidemiology
◦ What is health (and how location matters)
◦ What is a disease (and how to identify one)
◦ Quantifying disease occurrence
 Incidence versus prevalence
 Identifying the population
 Working with small area data
TOC

To study disease, we need measures of its
occurrence.

Some measures of disease occurrence
◦ Counts
◦ Prevalence
◦ Incidence
◦ Mortality
Measures of disease occurrence
DESCRIPTIVE
What?
What are the
health problems
of the
community?
What are the
attributes of
these illnesses?
Health and disease in the community
Who?
How many people
are affected?
What are the
attributes of
affected persons?
When?
Where?
Over what
Where do the
period of time? affected people
live, work or
spend leisure
time?
Each of the measures can be calculated for different
combinations of What? Who? When? and Where?
Each of the W’s needs to be defined carefully to get
comparable measures across a province or state, a nation,
the world.
Epidemiologic approaches

The prevalence of a disease is the proportion
of individuals in a population with the
disease (cases) at a specific point in time:
Number cases in population at specified time
Number of persons in population at that specified time

Prevalence is a proportion – range of 0 to 1

Removes the effect of total population size –
makes estimates from different populations
or over time more comparable.
Prevalence

Often expressed as a percent (%) – Prevalence *
100

Also often expressed as the prevalence per 1,000 or
10,000 or 100,000.

Prevalence * 1,000 = prevalence per 1,000.
Prevalence
(*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” woman)
1991
1995
2002
No Data
<10%
10%–14%
2006
15%–19%
20%–24%
≥25%
Obesity Trends Among U.S. Adults
Cases infected with the outbreak strain of Salmonella Saintpaul,
as of July 15, 2008 9 pm EDT. We would need to know the
population in each state in order to determine the prevalence.
Salmonella cases: Infected
Number of NEW cases in population DURING specified time
Number of persons AT RISK of disease in population during that specified time
If population
size is 3.81
million, then
652
 100,000
3,810,000
 .00017 100,000
 17.1
I
The incidence of a disease is the rate at which new cases occur in a
population during a specified period.
Incidence
Incidence of cases of infection with the outbreak strain as of July
15, 2008 9pm EDT
Salmonella cases: Incidence
Cases infected with the outbreak strain of Salmonella Saintpaul,
as of July 15, 2008 9pm EDT
Cases and Incidence – Salmonella

Incidence and prevalence measure different
aspects of disease occurrence
Prevalence
Incidence
Numerator:
All cases, no matter
how long diseased
Denominator: All persons in pop
Measures:
Presence of disease
Most useful: Resource allocation
Only NEW cases
Only persons at
risk of disease
Risk of disease
Risk, etiology
Incidence and Prevalence
Etiology: the study of a disease’s causes.

Numerator
◦ Number of deaths

Denominator
◦ Number of individuals in
population (how defined?)

Time interval
◦ 1-year: Annual Mortality Rate
◦ (typical to use an annual rate)

Specifier
◦ age, sex, race, etc.
Mortality Rate – Incidence of death
Mortality rates

For any measure, carefully defining both the
numerator and denominator is crucial for
interpretation.

In order for measures to be comparable across
studies, need consistent definition and reporting
strategies for numerator.

Also need consistent approaches for counting (or
estimating) the persons or person-time for the
denominator.
Importance of defining terms
AIDS cases, United States 1984-2000
Result of
new definition
1st Quarter of 1993:
Expansion of
surveillance case
definition
Prevalence numerator – case definition
Understanding population dynamics is crucial to epidemiology.
Demography = the study of population dynamics including
fertility, mortality and migration
Greek
English
epi
among
demos
people
logy
study
The “demi” in Epidemiology

GIS and health geography
◦ Major applications for GIS

Epidemiology
◦ What is health (and how location matters)
◦ What is a disease (and how to identify one)
◦ Quantifying disease occurrence
 Incidence vs prevalence
 Identifying the population
 Working with small area data
TOC

Developing multi-level models for
spatially-correlated data requires
confidence in the dependent data.

Data for disease mapping often consists
of disease counts and exposure levels
in small adjacent geographical areas.

The analysis of disease rates or counts for
small areas often involves a trade-off
between statistical stability of the
estimates and geographic precision.
Data considerations
56

Disease caused by a deficient diet or
failure of the body to absorb B complex
vitamins or an amino acid.

Common in certain parts of the world (in
people consuming large quantities of
corn), the disease is characterized by
scaly skin sores, diarrhea, mucosal
changes, and mental symptoms
(especially a schizophrenia-like
dementia). It may develop after
gastrointestinal diseases or alcoholism.
An example: Pellagra in the US
57
 A case study:
◦ They considered approximately 800 counties clustered
within 9 states in southern US
◦ For each county, data consisted of observed and
expected number of pellagra deaths
◦ For each county, they also had several county-specific
socio-economic characteristics and dietary factors
◦ % acres in cotton
◦ % farms under 20 acres
◦ Dairy cows per capita
◦ Access to mental hospital
◦ % Afro-American
◦ % single women
Multi-level data in spatial epidemiology
58
Which social, economical, behavioral, or
dietary factors best explain spatial
distribution of pellagra in southern US?
 Which of the above factors is more
important for explaining the history of
pellagra incidence in the US?
 To what extent have state-laws affected the
incidence of pellagra?

Scientific Questions
59
Definition of Standardized Mortality Ratio
60
Definition of the expected number of deaths
61
Crude Standardized Mortality Ratio (Observed/Expected) of
Pellagra Deaths in Southern USA in 1930 (Courtesy of Dr
Harry Marks)
62

For small areas, the Standardized
Mortality Ratio (SMR) can be very
instable and maps of SMR can be
misleading
◦ Spatial smoothing can improve stability

SMR are spatially correlated
◦ Spatially correlated random effects

Covariates available at different level of
spatial aggregation (county, State)
◦ Multi-level regression structure
Statistical Challenges
63

Spatial smoothing can reduce the random
noise in maps of observable data (or
disease rates)

Trade-off between geographic resolution
and the variability of the mapped estimates

Spatial smoothing as method for reducing
random noise and highlight meaningful
geographic patterns in the underlying risk
Spatial Smoothing
64

Shrinkage methods can be used to take
into account instable SMR for the small
areas

Idea is that:
◦ smoothed estimates for each area “borrow
strength” (precision) from data in other areas,
by an amount dependent on the precision of
the raw estimate of each area
Shrinkage Estimation
65

When population in area A is large
◦ Statistical error associated with observed
rate is small
◦ High credibility (weight) is given to observed
estimate
◦ Smoothed rate is close to observed rate

When population in area A is small
◦ Statistical error associated with observed
rate is large
◦ Little credibility (low weight) is given to
observed estimate
◦ Smoothed rate is “shrunk” towards mean
rate of surrounding areas
Shrinkage Estimation
66
Raw and smoothed SMR
67
Crude SMR
Smoothed SMR
SMR of pellagra deaths for 800
southern US counties in 1930
68

In epidemiology and demography, most
rates, such as incidence, prevalence,
mortality, are strongly age-dependent,
with risks rising (e.g. chronic diseases) or
declining (e.g. measles) with age. In part
this is biological (e.g. immunity
acquisition), and in part it reflects the
hazards of cumulative exposure, as is the
case for many forms of cancer. For many
purposes, age-specific comparisons may
be the most useful.
Ensuring comparability

However, comparisons of crude agespecific rates over time and between
populations may be very misleading if the
underlying age composition differs in the
populations being compared. Hence, for a
variety of purposes, a single ageindependent index, representing a set of
age-specific rates, may be more
appropriate. This is achieved by a process
of age standardization or age adjustment.
Ensuring comparability

The age-standardized
mortality rate is a
weighted average of
the age-specific
mortality rates per
100 000 persons,
where the weights are
the proportions of
persons in the
corresponding age
groups of the standard
population.
Standardizing

Spatial Analytic
Techniques for Medical
Geographers(Albert et
al., 2000)
Methodological toolboxes

GIS and health geography
◦ Major applications for GIS

Epidemiology
◦ What is health (and how location matters)
◦ What is a disease (and how to identify one)
◦ Quantifying disease occurrence
 Incidence versus prevalence
 Identifying the population
 Working with small area data
Summary